// BLOG / AI COMMERCIALIZATION
The Commercialization Gap: Why AI Pilots Need an Economic Test
Short answer. The commercialization gap is the distance between an AI capability and a business that pays for it. My framework focuses on four responsibilities: commercial ownership, pricing, a story buyers can repeat, and unit economics tested beyond the pilot. Enterprise AI returns also depend on technical reliability, integration and workflow fit.
MIT NANDA's preliminary "The GenAI Divide" report, hosted by AI News, described roughly 95 percent of enterprise generative AI pilots as delivering no measurable return. Its January to June 2025 research reviewed 300 disclosed initiatives, interviewed 52 organizations and gathered 153 survey responses. It identified integration, brittle workflows and a learning gap, not a single commercial cause. Separately, Gartner forecast in July 2024 that at least 30 percent of generative AI projects would be abandoned after proof of concept by the end of 2025, citing data quality, risk controls, costs and unclear value. A forecast of abandonment and a preliminary study of returns measure different things.
A working pilot still needs an operating plan
When an AI initiative stalls, test the failure before changing the model. Fine-tuning or better retrieval may help a reliability problem. They will not decide who owns the outcome, how much a customer will pay or what delivery costs. Those are the commercial questions this framework addresses; the MIT study does not establish that all unsuccessful pilots technically worked.
A startup with flat revenue needs customers who buy and renew. An enterprise team running an internal pilot may be pursuing savings or faster work rather than product revenue. Both need an owner and an economic test, but their measures of success differ.
The four failures that open the gap
These are four commercial failures to check. They are my operating framework, not a diagnosis assigned to every pilot in the research.
1. No owner of the crossing. Engineering owns capability and marketing owns the story, but neither may own whether the product earns revenue and at what cost. Assign that responsibility explicitly. The Chief AI Officer guide covers how that owner connects the technical and commercial decisions.
2. Pricing that ignores compute. AI inference adds a variable delivery cost. Under a flat seat price, margin can fall when usage costs outgrow revenue. Stable usage and a small compute share can still support seats. That is the failure I work through in AI pricing has to respect compute reality.
3. A story that does not travel. A buyer may need to explain the product to a CFO or committee after your meeting. Give them a clear account of the customer, problem and outcome, plus supporting evidence. The market buys a story it can repeat covers that retelling test.
4. Unit economics that break at scale. A small pilot can leave serving and support costs hidden in another budget. Test the economics at the scale you intend to serve: account costs, retention and gross margin. Why a working AI product still struggles to make money covers those checks.
Cheaper models do not settle the commercial questions
Cheaper models lower the cost of trying a pilot. They do not decide what it is worth, who pays or which workflow must change. Before extending the pilot, name the intended revenue or savings, the delivery costs and the evidence needed to continue. The studies cited here do not provide a time series showing that the failure rate is rising or falling.
Closing the gap
Closing the commercialization gap takes an accountable owner, pricing built on delivery cost, a story buyers can repeat and economics tested at the scale you intend to serve. For an internal enterprise project, use the savings or operating improvement it is meant to deliver instead of a product price. These checks give you a basis to change, extend or stop the work; they do not guarantee a return.
The AI commercialization guide covers the pricing and business decisions. The Chief AI Officer guide covers who owns them. Assign the owner and the economic test before funding another pilot.
Frequently asked questions
What is the commercialization gap?
The commercialization gap is the distance between an AI capability and a business that pays for it. My framework covers commercial ownership, pricing, a story buyers can repeat and unit economics beyond the pilot. Technical reliability, integration and workflow fit remain separate requirements.
What percentage of enterprise AI projects fail?
MIT NANDA's preliminary January to June 2025 research described roughly 95 percent of enterprise generative AI pilots as delivering no measurable return. Gartner's July 2024 forecast predicted at least 30 percent of generative AI projects would be abandoned after proof of concept by the end of 2025. Those figures have different methods and outcomes; neither proves that technically successful pilots failed solely because of commercialization.
Why do AI pilots fail to deliver ROI?
MIT NANDA's preliminary research points to integration, workflow and learning problems. Gartner's forecast also cited data quality, risk controls, cost and unclear value. My commercial framework adds questions about ownership, pricing, narrative and unit economics. Check which problem the pilot has rather than assuming a new model or a new price will fix it.
About the author
Jeff Brokaw is a Certified Chief AI Officer and technical-commercial operator who rebuilds the commercial layer of technically complex companies. He has been building AI systems commercially since 2016. He was the first marketing hire twice, at companies tied to $850M+ in associated exits. He reframed institutional Bitcoin mining as energy-backed digital infrastructure; a $114M institutional raise came together in under thirty days. In one advanced-materials engagement, he built the commercial engine behind $185M in new-business revenue and wrote the strategic narrative behind a $50M Defense Production Act award.